At least some known closed-loop neuromodulation devices use biophysical signal feedback to perform automatic adjustment of stimulation parameters or titration of stimulation, which can facilitate maximizing therapeutic effectiveness, increasing power efficiency, and minimizing side effects. For example, for treatment of medically-refractory epilepsy, at least some known neuromodulation devices apply electrical stimulation in the brain only when epileptic neural activity is detected.
Several feedback signals have been proposed for closed-loop deep brain stimulation (DBS) devices that treat Parkinson's disease (PD), essential tremor (ET), or other movement disorders. One suitable signal is the local field potential (LFP), which represents synchronized neuronal oscillations within brain circuits and can be measured with a DBS lead. Pathological LFP activity may be correlated to motor symptoms, such as the known relationship between elevated beta band LFP oscillations (13-35 Hertz (Hz)) and bradykinesia/rigidity in PD. Another potential feedback signal is body motion measurement, which can indicate the presence or absence of tremor or other symptoms of PD or ET.
However, biophysical signals are generally noisy, making data analysis difficult in many cases. If a signal is periodic, one solution is to perform ensemble averaging to increase a signal-to-noise (SNR) ratio. This requires calculating the mean over time of individual signal responses, which increases SNR by the square root of the number of responses used in the average. However, there is a lack of external trigger events that can be used to align individual responses for ensemble averaging of LFP and/or tremor activity, since these are derived from spontaneous processes.